Advances in Artificial Intelligence-Based Depression Diagnosis: A Systematic Review

This study systematically reviews the current status and recent advances in intelligent depression detection, aiming to provide insights for applying artificial intelligence in mental health. Using a systematic review approach, we analyze detection methods based on multiple data types including voice, facial expressions, body signals, and social media texts, while examining how algorithms have evolved from traditional machine learning to deep learning. Results show that AI technology has clear benefits in improving detection accuracy, reducing costs, and enabling early warning systems. Current research still faces important challenges in data collection, technical reliability, clinical use, and privacy concerns. Future work should focus on combining knowledge from different fields, implementing systems in clinical practice, and developing standards for wider adoption.